How to build effective machine learning models?

Nowadays, the popularity of machine learning is on the rise. More and more organizations are using this technology in order to predict customer demand, boost inventory forecasting, and optimize operations. According to a recent research study, AI received more than $8 billion worth of investment in 2016. Let's take a look at 7 tips that can help organizations get the most out of machine learning.

1. Review the Data

It takes time to prepare a training data set. During this process, mistakes may happen from time to time. Therefore, before you start working on a model, we suggest that you perform a data review. This will help you find out if the required data is free of errors.

2. Slice the Given Data

Typically, there are different structures in data. Therefore, you may want to slice your data just like you slice a pizza. Your goal is to build separate models for the slices. After you have identified a target, you may build a decision tree. Then you can build different models for the segments.

3. Make use of Simple Models

It's important to build complex models so you can extract information from the data. Simple models are much easier for deployment. Plus, they make the explanation process much easier for primary business stakeholders.

What you need to do is build simple models with a decision and regression trees. Plus, you should use an ensemble model or gradient boosting to ensure the functionality of your models.

4. Identify Rare Events

Often, machine learning requires unbalanced data. Therefore, it can be hard for you to properly classify rare events. If you want to counteract this, we suggest that you build a biased training data through under or oversampling.

This will help balance your training data. Aside from this, the higher events ratio can help the algorithm differentiate between the event signals. Decision processing is another strategy to put much greater weight on event classification.

5. Combine Several Models

Typically, data scientists use different algorithms, such as random forests and gradient boosting in order to construct many models. Although these models generalize well, you can choose ones that will provide a better fit in case of certain data boundaries. An easy way to overcome this problem is to combine various modeling algorithms.

6. Deploy the Models

Often, it takes a few weeks or months to deploy models. Some models don't get deployed at all. For better results, you may want to determine the objectives of the business to manage the data and then monitor the models. Aside from this, you can use tools for capturing and binding data.

7. Autotune the Models

You need to assign algorithm options known as hyperparameters before you build a machine-learning model. Actually, autotuning helps identify proper hyperactive parameters in a short period. And this is one of the greatest benefits of autotuning.

In short, these are the 7 tips that may help you develop effective machine learning models. Hopefully, you will find these tips greatly helpful throughout your projects.